Electricity consumption in residential markets will undergo fundamental changes in the next decade due to the emergence of smart appliances and home automation. A key requirement for the smart appliances within the smart grid framework is the demand response (DR). The North American Electric Reliability Corporation has defined demand response as changes in electricity usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity (price-responsive DR), or to incentive payments designed to induce lower electricity use at time of high wholesale market prices or when system reliability is jeopardized (curtailable DR).
Electric utility companies typically use hourly real-time price (RTP) or day-ahead price (DAP) structure in their dynamic pricing programs. In North America, Ameren Focused Energy, serving about 2.4 million electric customers in Illinois and Missouri, has very detailed RTP and DAP tariffs posted on their website since Jun. 1, 2008 for both day-ahead and real-time markets. The day-ahead market produces financially binding schedules for the production and consumption of electricity one day before the operating day. The real-time market reconciles any differences between the amounts of energy scheduled day-ahead and the real-time load, market participant re-offers, hourly self-schedules, self-curtailments and any changes in general, real-time system conditions. Therefore, the DAP structure provides valuable information for price-sensitive loads while the RTP structure gives useful information for curtail operations.
For a typical home in the United States, home appliances are responsible for an important part of the energy bills. These appliances may include home heating, ventilation, and air conditioning system (HVAC), water heaters, clothes washers and dryers, dishwashers, refrigerator and freezers, electric stoves and/or ovens, coffee maker, home electric drive vehicle charging system, and lights, for example. For each energy consuming appliance, key factors affecting household energy consumption include: 1) appliance load level, 2) when and how long an appliance is used, and 3) how much unwanted heat could be generated when using the appliance. For a flat electricity price structure, a customer would use an appliance whenever it is needed. However, for a dynamic electricity price structure, customers are encouraged to optimize energy consumption of their DR capable appliances.
Typically, the HVAC system is one of the more challenging appliances for a DR strategy. Traditionally, the thermostat of a HVAC unit is set at 71° or 72° for a typical house in the United States. However, in a dynamic price framework, the thermostat setting is regulated according to the real-time price information.
In modeling energy consumption of a residential house, the amount of energy consumed by the HVAC system is typically the most dominant part. The heat load that the HVAC system must overcome is mainly generated in three ways: conduction, convection, and radiation. In most conventional DR studies, the heat load of a residential house is computed based on simplified approaches that typically only consider conduction. However, actual energy consumption of a residential house is much more complicated, which can be affected by geographical location, design architecture, window arrangements, insulation materials, occupants, weather, season, etc. and can change from one day to another.
A key component for a successful price-responsive DR program is a home automation system (HAS). Basically, a HAS receives information about weather forecast, dynamic electricity pricing, device operating characteristics, usage requests, etc., and autonomously makes control decisions and sends control actions to smart appliances.
However, a great challenge for the HAS to establish an optimal price-responsive DR strategy is how to accurately model and estimate the energy consumption of a residential house in variable weather conditions and a dynamic pricing environment. As noted above, in existing technologies, many DR techniques are developed based on simplified energy consumption models. For example, a strategy to minimize the cost for electricity consumption has been proposed in which the energy consumption of a house is modeled based on simple conduction heat transfer equations. For example, a simplified equivalent and thermal parameter (ETP) modeling approach is used in GridLAB-D, a distribution system simulator, to estimate thermal loads of a residential house based on first principles. Further, a quasi-steady-state approach has been adopted to estimate hourly building electricity demand, in which the building thermal model is built based on an equivalent resistance-capacitance network. The hourly energy consumption is determined through an optimization strategy under the constraints of several predefined customer load levels which include maximum and minimum hourly demands, minimum daily consumption, and ramping up and down limits.
Other approaches to DR strategies include a heuristic approach and an “optimal” DR approach. FIG. 1 illustrates a flow diagram of a heuristic DR strategy for operation of an HVAC system during the summer. The heuristic DR strategy is a variable temperature setting approach. During the summer time, for instance, the air conditioner should be operated the coolest possible near the lower boundary, Tmin of the ASHRAE summer comfort zone when the RTP is lower than a predefined value. On the other hand, the HVAC is operated the hottest possible near the upper boundary, Tmax, of the ASHRAE summer comfort zone. The HVAC is operated between these two boundaries depending on the RTP tariff. In FIG. 1, Preal is the RTP for the current time frame i. Pmin is a minimum price point, Pmax is a maximum price point, and P1-Pn are intermediate price points between Pmin and Pmax.
Assuming there are n temperature settings between Tmax and Tmin, then, the price and thermostat settings for summer time are calculated by equations (1) and (2) below.Pi=Pmax−PRdiff tan h(k·i·PRdiff)  (1)Ti=Tmax−(Tmax−Tmin)/n·i  (2)Ti=Tmax+(Tmax−Tmin)/n·i  (3)The basic concept is that the thermostat setting is determined through a combined consideration of maximum and minimum real-time price and price distribution over a day. In equation (1) above, k is a constant obtained from a price distribution study for different seasons, and PRdiff=Pmax−Pmin, where Pmax and Pmin correspond to maximum and minimum electricity prices of a day, respectively. For winter time, a modification is necessary with Tmax and Tmin corresponding to Pmin and Pmax, respectively, and the price and thermostat settings are calculated by equations (1) and (3) above. Similarly, T1, T2, and Tn correspond to P1, P2, and Pn, respectively.
The DR strategy obtained according to FIG. 1 may be good some days but bad other days. Another problem with the heuristic DR strategy is that the algorithm is unable to take the advantage of low price periods to pre-cool down a house significantly.
The optimal DR approach attempts to determine the optimal thermostat setting strategy for a given dynamic price of a day, and thus is more efficient than the heuristic DR approach. To develop an optimal DR strategy, the following nonlinear programming formulation is used:
                                          Minimize            ⁢                                                  ⁢                          :                        ⁢                                                  ⁢            C                    =                                    ∑              i                        ⁢                                                  ⁢                                          p                i                            ·                              Q                i                                                    ⁢                                  ⁢                                            Subject              ⁢                                                          ⁢              to              ⁢                                                          ⁢                              :                            ⁢                                                          ⁢              0                        ≤                          Q              i                        ≤                          Q              max                                ,                                    T              min                        ≤                          T              i              I                        ≤                          T              max                                                          (        4        )            where Tmin=Tideal−d, Tmax=Tideal+d, d is the acceptable temperature deviation, i represents a time slot in one hour, TiI is the room temperature in hour i, C is the electricity cost during a day, pi stands for the electricity price in hour i, Qi signifies the energy consumed by the HVAC unit in hour i, and Qmax denotes the maximum energy that can be consumed by the HVAC unit. Traditionally, the energy consumed by the HVAC Qi is modeled as a function of room temperature TiI and outdoor temperature TiO. Equation (5) below shows a simplified thermal model of a residential house:Ti+1I=ε·TiI+(1−ε)(TiO−η·Qi/A)  (5)where η is the efficiency of the HVAC unit, Σ is the system inertia, and A is the thermal conductivity. However, in reality, the relationship between indoor and outdoor temperatures and energy consumed by the HVAC unit is much more complicated than Equation (5) so that actual energy consumption of the HVAC unit could deviate greatly from results generated by using Equation (5), which affects the DR efficiency. Therefore, an intelligent mechanism that can identify and update an energy consumption model daily for a residential house is needed for developing an optimal DR strategy.